{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# k-近邻算法"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一般流程：\n",
    "1. 收集数据：可以使用任何方法\n",
    "2. 准备数据：距离计算所需要的数值，最好是结构化的数据格式\n",
    "3. 分析数据：可以使用任何方法\n",
    "4. 训数据：此步骤不适合k-近邻算法\n",
    "5. 测试数据：计算错误率\n",
    "6. 使用算法：首先要输入样本数据和结构化的输出结果，然后运行k-近邻算法判定输入数据份分别属于哪个分类，最后应用对计算出的分类执行后续\n",
    "处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#导入包\n",
    "from numpy import *\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createDataSet():\n",
    "    group = array([[1.0,1.1], [1.0,1.0], [0,0], [0,0.1]])\n",
    "    labels = ['A', 'A', 'B', 'B']\n",
    "    return group, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "group_test,labels_test = createDataSet()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['A', 'A', 'B', 'B']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classify0(inX, dataSet, labels, k):\n",
    "    dataSetSize = dataSet.shape[0] #获取矩阵dataSet行数\n",
    "    diffMat = tile(inX, (dataSetSize,1)) - dataSet #得到 inX 跟 dataSet 矩阵每一行的差值，生成一个 与 dataSet 相同维数的矩阵\n",
    "    sqDiffMat = diffMat**2 # 矩阵每个元素求平方\n",
    "    sqDistances = sqDiffMat.sum(axis=1) #求和\n",
    "    distances = sqDistances**0.5 #开方\n",
    "    sortedDistIndicies = distances.argsort() #从小到大排序，获取每一个元素排序前的下标，存在 sortedDistIndicies\n",
    "    classCount={}\n",
    "    for i in range(k):\n",
    "        voteIlabel = labels[sortedDistIndicies[i]]\n",
    "        classCount[voteIlabel] = classCount.get(voteIlabel,0) + 1\n",
    "    sortedClassCount = sorted(classCount.items(),\n",
    "                             key=operator.itemgetter(1), reverse=True)\n",
    "    return sortedClassCount[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'B'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classify0([0,0], group_test, labels_test, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将文本记录转换为NumPy的解析程序\n",
    "def file2matrix(filename):\n",
    "    fr = open(filename)\n",
    "    arrayOLines = fr.readlines()\n",
    "    numberOfLines = len(arrayOLines)\n",
    "    returnMat = zeros((numberOfLines, 3))\n",
    "    classLabelVector = []\n",
    "    index = 0\n",
    "    for line in arrayOLines:\n",
    "        line = line.strip()\n",
    "        listFromLine = line.split('\\t')\n",
    "        returnMat[index,:] = listFromLine[0:3]\n",
    "        classLabelVector.append(int(listFromLine[-1]))\n",
    "        index += 1\n",
    "    return returnMat,classLabelVector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.0920000e+04, 8.3269760e+00, 9.5395200e-01],\n",
       "       [1.4488000e+04, 7.1534690e+00, 1.6739040e+00],\n",
       "       [2.6052000e+04, 1.4418710e+00, 8.0512400e-01],\n",
       "       ...,\n",
       "       [2.6575000e+04, 1.0650102e+01, 8.6662700e-01],\n",
       "       [4.8111000e+04, 9.1345280e+00, 7.2804500e-01],\n",
       "       [4.3757000e+04, 7.8826010e+00, 1.3324460e+00]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingDataMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " 2,\n",
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       " 2,\n",
       " 2,\n",
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       " 2,\n",
       " 2,\n",
       " 3,\n",
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       " 2,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 3,\n",
       " 3,\n",
       " 3]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datingLabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.scatter(datingDataMat[:,1], datingDataMat[:,2],\n",
    "          15.0*array(datingLabels), 15.0*array(datingLabels))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#归一化\n",
    "def autoNorm(dataSet):\n",
    "    minVals = dataSet.min(0)\n",
    "    maxVals = dataSet.max(0)\n",
    "    ranges = maxVals - minVals\n",
    "    normDataSet = zeros(shape(dataSet))\n",
    "    m = dataSet.shape[0]\n",
    "    normDataSet = dataSet - tile(minVals, (m,1))\n",
    "    normDataSet = normDataSet/tile(ranges, (m,1))\n",
    "    return normDataSet, ranges, minVals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "normMat, ranges, minVals = autoNorm(datingDataMat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.44832535, 0.39805139, 0.56233353],\n",
       "       [0.15873259, 0.34195467, 0.98724416],\n",
       "       [0.28542943, 0.06892523, 0.47449629],\n",
       "       ...,\n",
       "       [0.29115949, 0.50910294, 0.51079493],\n",
       "       [0.52711097, 0.43665451, 0.4290048 ],\n",
       "       [0.47940793, 0.3768091 , 0.78571804]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normMat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.1273000e+04, 2.0919349e+01, 1.6943610e+00])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def datingClassTest():\n",
    "    hoRatio = 0.10\n",
    "    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')\n",
    "    normMat,ranges,minVals = autoNorm(datingDataMat)\n",
    "    m = normMat.shape[0]\n",
    "    numTestVecs = int(m*hoRatio)\n",
    "    errorCount = 0.0\n",
    "    for i in range(numTestVecs):\n",
    "        classfierResult = classify0(normMat[i,:],normMat[numTestVecs:m,:],\n",
    "                                   datingLabels[numTestVecs:m],3)\n",
    "        print(\"the classifier came back with: %d, the real answer is: %d\" % \n",
    "             (classfierResult, datingLabels[i]))\n",
    "        if classfierResult != datingLabels[i]:\n",
    "            errorCount += 1.0\n",
    "    print(\"the total error rate is:%f\" % (errorCount/float(numTestVecs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 1\n",
      "the total error rate is:0.050000\n"
     ]
    }
   ],
   "source": [
    "datingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifyPerson():\n",
    "    resultList = ['not at all', 'in small doses', 'in large doses']\n",
    "    percentTats = float(input(\"percentage of time spent playing video games?\"))\n",
    "    ffMiles = float(input(\"frequent fliter miles earned per year?\"))\n",
    "    iceCream = float(input(\"liters of ice cream consumed per year?\"))\n",
    "    datingDataMat,datingLabels = file2matrix('datingTestSet2.txt')\n",
    "    norMat, ranges, minVals = autoNorm(datingDataMat)\n",
    "    inArr = array([ffMiles, percentTats, iceCream])\n",
    "    classifierResutl = classify0((inArr-minVals)/ranges, normMat, datingLabels,3)\n",
    "    print(\"You will probably like this person: \", resultList[classifierResutl - 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "percentage of time spent playing video games?10\n",
      "frequent fliter miles earned per year?10000\n",
      "liters of ice cream consumed per year?0.5\n",
      "You will probably like this person:  in small doses\n"
     ]
    }
   ],
   "source": [
    "classifyPerson()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import listdir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def img2vector(filename):\n",
    "    returnVect = zeros((1,1024))\n",
    "    fr = open(filename)\n",
    "    for i in range(32):\n",
    "        lineStr = fr.readline()\n",
    "        for j in range(32):\n",
    "            returnVect[0,32*i+j] = int(lineStr[j])\n",
    "    return returnVect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def handwritingClassTest():\n",
    "    hwLabels = []\n",
    "    trainingFileList = listdir('trainingDigits')\n",
    "    m = len(trainingFileList)\n",
    "    trainingMat = zeros((m,1024))\n",
    "    for i in range(m):\n",
    "        fileNameStr = trainingFileList[i]\n",
    "        fileStr = fileNameStr.split('.')[0]\n",
    "        classNumStr = int(fileStr.split('_')[0])\n",
    "        hwLabels.append(classNumStr)\n",
    "        trainingMat[i,:] = img2vector('trainingDigits/%s' % fileNameStr)\n",
    "    testFileList = listdir('testDigits')\n",
    "    errorCount = 0.0\n",
    "    mTest = len(testFileList)\n",
    "    for i in range(mTest):\n",
    "        fileNameStr = testFileList[i]\n",
    "        fileStr = fileNameStr.split('.')[0]\n",
    "        classNumStr = int(fileStr.split('_')[0])\n",
    "        vectorUnderTest = img2vector('testDigits/%s' % fileNameStr)\n",
    "        classifierResult = classify0(vectorUnderTest,trainingMat, hwLabels, 3)\n",
    "        print(\"the classifier came back with: %d, the real answer is: %d\" % \n",
    "             (classifierResult, classNumStr))\n",
    "        if(classifierResult != classNumStr): errorCount += 1.0\n",
    "    print(\"\\nthe total number of error is: %d\" % errorCount)\n",
    "    print(\"\\n the total error rate is: %f\" % (errorCount/float(mTest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
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      "the classifier came back with: 7, the real answer is: 7\n",
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      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
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      "the classifier came back with: 7, the real answer is: 7\n",
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      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
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      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 7, the real answer is: 7\n",
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      "the classifier came back with: 3, the real answer is: 3\n",
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      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 6, the real answer is: 6\n",
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      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 5, the real answer is: 5\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 5, the real answer is: 5\n",
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      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 2, the real answer is: 2\n",
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      "the classifier came back with: 2, the real answer is: 2\n",
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      "the classifier came back with: 1, the real answer is: 1\n",
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      "the classifier came back with: 1, the real answer is: 1\n",
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      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 4, the real answer is: 4\n",
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      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 1, the real answer is: 1\n",
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      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 6, the real answer is: 6\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 3, the real answer is: 3\n",
      "the classifier came back with: 8, the real answer is: 8\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 5, the real answer is: 5\n",
      "the classifier came back with: 9, the real answer is: 9\n",
      "the classifier came back with: 2, the real answer is: 2\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 9, the real answer is: 3\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 0, the real answer is: 0\n",
      "the classifier came back with: 4, the real answer is: 4\n",
      "the classifier came back with: 1, the real answer is: 1\n",
      "the classifier came back with: 7, the real answer is: 7\n",
      "\n",
      "the total number of error is: 10\n",
      "\n",
      " the total error rate is: 0.010571\n"
     ]
    }
   ],
   "source": [
    "handwritingClassTest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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